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Recent research has demonstrated that feature attribution methods for deepnetworks can themselves be incorporated into training; these attribution priorsoptimize for a model whose attributions have certain desirable properties --most frequently, that particular features are important or unimportant. Theseattribution priors are often based on attribution methods that are notguaranteed to satisfy desirable interpretability axioms, such as completenessand implementation invariance. Here, we introduce attribution priors tooptimize for higher-level properties of explanations, such as smoothness andsparsity, enabled by a fast new attribution method formulation called expectedgradients that satisfies many important interpretability axioms. This improvesmodel performance on many real-world tasks where previous attribution priorsfail. Our experiments show that the gains from combining higher-levelattribution priors with expected gradients attributions are consistent acrossimage, gene expression, and health care data sets. We believe this workmotivates and provides the necessary tools to support the widespread adoptionof axiomatic attribution priors in many areas of applied machine learning. Theimplementations and our results have been made freely available to academiccommunities.

Improving performance of deep learning models with axiomatic attribution priors and expected gradients